Overview

Dataset statistics

Number of variables21
Number of observations10127
Missing cells2730
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory168.0 B

Variable types

Numeric15
Categorical6

Alerts

customer_age is highly overall correlated with customer_relationship_lengthHigh correlation
customer_relationship_length is highly overall correlated with customer_ageHigh correlation
customer_available_credit_limit is highly overall correlated with remaining_credit_limitHigh correlation
credit_card_debt_balance is highly overall correlated with average_utilizationHigh correlation
remaining_credit_limit is highly overall correlated with customer_available_credit_limit and 1 other fieldsHigh correlation
total_transaction_amount is highly overall correlated with total_transaction_countHigh correlation
total_transaction_count is highly overall correlated with total_transaction_amountHigh correlation
average_utilization is highly overall correlated with credit_card_debt_balance and 1 other fieldsHigh correlation
customer_sex is highly overall correlated with customer_salary_rangeHigh correlation
customer_salary_range is highly overall correlated with customer_sexHigh correlation
credit_card_classification is highly imbalanced (79.2%)Imbalance
customer_age has 624 (6.2%) missing valuesMissing
customer_sex has 1018 (10.1%) missing valuesMissing
customer_salary_range has 681 (6.7%) missing valuesMissing
total_transaction_amount has 407 (4.0%) missing valuesMissing
customer_number_of_dependents has 904 (8.9%) zerosZeros
contacts_in_last_year has 399 (3.9%) zerosZeros
credit_card_debt_balance has 2470 (24.4%) zerosZeros
average_utilization has 2470 (24.4%) zerosZeros

Reproduction

Analysis started2023-05-01 12:56:41.819381
Analysis finished2023-05-01 12:57:13.166809
Duration31.35 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct10076
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean550508.99
Minimum100069
Maximum999911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-05-01T14:57:13.272101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum100069
5-th percentile146834.4
Q1323605
median552548
Q3777326
95-th percentile955268.1
Maximum999911
Range899842
Interquartile range (IQR)453721

Descriptive statistics

Standard deviation261237.66
Coefficient of variation (CV)0.4745384
Kurtosis-1.2132735
Mean550508.99
Median Absolute Deviation (MAD)227313
Skewness-0.0012450197
Sum5.5750045 × 109
Variance6.8245113 × 1010
MonotonicityNot monotonic
2023-05-01T14:57:13.396903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
585752 2
 
< 0.1%
551232 2
 
< 0.1%
515651 2
 
< 0.1%
101989 2
 
< 0.1%
167216 2
 
< 0.1%
113558 2
 
< 0.1%
356515 2
 
< 0.1%
944269 2
 
< 0.1%
901904 2
 
< 0.1%
260129 2
 
< 0.1%
Other values (10066) 10107
99.8%
ValueCountFrequency (%)
100069 1
< 0.1%
100076 1
< 0.1%
100091 1
< 0.1%
100095 1
< 0.1%
100096 1
< 0.1%
100117 1
< 0.1%
100331 1
< 0.1%
100344 1
< 0.1%
100689 1
< 0.1%
100769 1
< 0.1%
ValueCountFrequency (%)
999911 1
< 0.1%
999876 1
< 0.1%
999811 1
< 0.1%
999755 1
< 0.1%
999673 1
< 0.1%
999640 1
< 0.1%
999474 1
< 0.1%
999394 1
< 0.1%
999232 1
< 0.1%
999218 1
< 0.1%

customer_age
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)0.5%
Missing624
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean46.3179
Minimum26
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-05-01T14:57:13.510742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33
Q141
median46
Q352
95-th percentile60
Maximum73
Range47
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.0012274
Coefficient of variation (CV)0.1727459
Kurtosis-0.28227809
Mean46.3179
Median Absolute Deviation (MAD)6
Skewness-0.036413591
Sum440159
Variance64.019641
MonotonicityNot monotonic
2023-05-01T14:57:13.631260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
44 472
 
4.7%
49 465
 
4.6%
46 459
 
4.5%
45 457
 
4.5%
48 453
 
4.5%
47 446
 
4.4%
43 446
 
4.4%
50 420
 
4.1%
42 400
 
3.9%
52 366
 
3.6%
Other values (35) 5119
50.5%
(Missing) 624
 
6.2%
ValueCountFrequency (%)
26 74
0.7%
27 32
 
0.3%
28 23
 
0.2%
29 53
 
0.5%
30 67
 
0.7%
31 86
0.8%
32 97
1.0%
33 121
1.2%
34 138
1.4%
35 168
1.7%
ValueCountFrequency (%)
73 1
 
< 0.1%
70 1
 
< 0.1%
68 1
 
< 0.1%
67 4
 
< 0.1%
66 2
 
< 0.1%
65 94
0.9%
64 40
0.4%
63 59
0.6%
62 86
0.8%
61 86
0.8%

customer_sex
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1018
Missing (%)10.1%
Memory size79.2 KiB
F
4838 
M
4271 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9109
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
F 4838
47.8%
M 4271
42.2%
(Missing) 1018
 
10.1%

Length

2023-05-01T14:57:13.739331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T14:57:13.829190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
f 4838
53.1%
m 4271
46.9%

Most occurring characters

ValueCountFrequency (%)
F 4838
53.1%
M 4271
46.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9109
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 4838
53.1%
M 4271
46.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 9109
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 4838
53.1%
M 4271
46.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 4838
53.1%
M 4271
46.9%
Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3462032
Minimum0
Maximum5
Zeros904
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-05-01T14:57:13.894009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2989083
Coefficient of variation (CV)0.55362142
Kurtosis-0.68301665
Mean2.3462032
Median Absolute Deviation (MAD)1
Skewness-0.020825536
Sum23760
Variance1.6871629
MonotonicityNot monotonic
2023-05-01T14:57:13.979702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2732
27.0%
2 2655
26.2%
1 1838
18.1%
4 1574
15.5%
0 904
 
8.9%
5 424
 
4.2%
ValueCountFrequency (%)
0 904
 
8.9%
1 1838
18.1%
2 2655
26.2%
3 2732
27.0%
4 1574
15.5%
5 424
 
4.2%
ValueCountFrequency (%)
5 424
 
4.2%
4 1574
15.5%
3 2732
27.0%
2 2655
26.2%
1 1838
18.1%
0 904
 
8.9%
Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Graduate
3128 
High School
2013 
Unknown
1519 
Uneducated
1487 
College
1013 
Other values (2)
967 

Length

Max length13
Median length11
Mean length8.9392713
Min length7

Characters and Unicode

Total characters90528
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh School
2nd rowGraduate
3rd rowHigh School
4th rowHigh School
5th rowHigh School

Common Values

ValueCountFrequency (%)
Graduate 3128
30.9%
High School 2013
19.9%
Unknown 1519
15.0%
Uneducated 1487
14.7%
College 1013
 
10.0%
Post-Graduate 516
 
5.1%
Doctorate 451
 
4.5%

Length

2023-05-01T14:57:14.080325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T14:57:14.195240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
graduate 3128
25.8%
high 2013
16.6%
school 2013
16.6%
unknown 1519
12.5%
uneducated 1487
12.2%
college 1013
 
8.3%
post-graduate 516
 
4.3%
doctorate 451
 
3.7%

Most occurring characters

ValueCountFrequency (%)
a 9226
 
10.2%
e 9095
 
10.0%
o 7976
 
8.8%
d 6618
 
7.3%
t 6549
 
7.2%
n 6044
 
6.7%
u 5131
 
5.7%
r 4095
 
4.5%
l 4039
 
4.5%
h 4026
 
4.4%
Other values (15) 27729
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 75343
83.2%
Uppercase Letter 12656
 
14.0%
Space Separator 2013
 
2.2%
Dash Punctuation 516
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9226
12.2%
e 9095
12.1%
o 7976
10.6%
d 6618
8.8%
t 6549
8.7%
n 6044
8.0%
u 5131
6.8%
r 4095
 
5.4%
l 4039
 
5.4%
h 4026
 
5.3%
Other values (6) 12544
16.6%
Uppercase Letter
ValueCountFrequency (%)
G 3644
28.8%
U 3006
23.8%
S 2013
15.9%
H 2013
15.9%
C 1013
 
8.0%
P 516
 
4.1%
D 451
 
3.6%
Space Separator
ValueCountFrequency (%)
2013
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 516
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 87999
97.2%
Common 2529
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9226
 
10.5%
e 9095
 
10.3%
o 7976
 
9.1%
d 6618
 
7.5%
t 6549
 
7.4%
n 6044
 
6.9%
u 5131
 
5.8%
r 4095
 
4.7%
l 4039
 
4.6%
h 4026
 
4.6%
Other values (13) 25200
28.6%
Common
ValueCountFrequency (%)
2013
79.6%
- 516
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9226
 
10.2%
e 9095
 
10.0%
o 7976
 
8.8%
d 6618
 
7.3%
t 6549
 
7.2%
n 6044
 
6.7%
u 5131
 
5.7%
r 4095
 
4.5%
l 4039
 
4.5%
h 4026
 
4.4%
Other values (15) 27729
30.6%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Married
4687 
Single
3943 
Unknown
749 
Divorced
748 

Length

Max length8
Median length7
Mean length6.6845068
Min length6

Characters and Unicode

Total characters67694
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowUnknown
3rd rowMarried
4th rowMarried
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 4687
46.3%
Single 3943
38.9%
Unknown 749
 
7.4%
Divorced 748
 
7.4%

Length

2023-05-01T14:57:14.314483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T14:57:14.422417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
married 4687
46.3%
single 3943
38.9%
unknown 749
 
7.4%
divorced 748
 
7.4%

Most occurring characters

ValueCountFrequency (%)
r 10122
15.0%
i 9378
13.9%
e 9378
13.9%
n 6190
9.1%
d 5435
8.0%
M 4687
6.9%
a 4687
6.9%
l 3943
 
5.8%
g 3943
 
5.8%
S 3943
 
5.8%
Other values (7) 5988
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57567
85.0%
Uppercase Letter 10127
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 10122
17.6%
i 9378
16.3%
e 9378
16.3%
n 6190
10.8%
d 5435
9.4%
a 4687
8.1%
l 3943
 
6.8%
g 3943
 
6.8%
o 1497
 
2.6%
k 749
 
1.3%
Other values (3) 2245
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
M 4687
46.3%
S 3943
38.9%
U 749
 
7.4%
D 748
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 67694
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 10122
15.0%
i 9378
13.9%
e 9378
13.9%
n 6190
9.1%
d 5435
8.0%
M 4687
6.9%
a 4687
6.9%
l 3943
 
5.8%
g 3943
 
5.8%
S 3943
 
5.8%
Other values (7) 5988
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 10122
15.0%
i 9378
13.9%
e 9378
13.9%
n 6190
9.1%
d 5435
8.0%
M 4687
6.9%
a 4687
6.9%
l 3943
 
5.8%
g 3943
 
5.8%
S 3943
 
5.8%
Other values (7) 5988
8.8%

customer_salary_range
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.1%
Missing681
Missing (%)6.7%
Memory size79.2 KiB
below 40K
3327 
40-60K
1666 
80-120K
1436 
60-80K
1302 
Unknown
1030 

Length

Max length13
Median length9
Mean length7.8253229
Min length6

Characters and Unicode

Total characters73918
Distinct characters21
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40-60K
2nd rowbelow 40K
3rd row80-120K
4th rowbelow 40K
5th row80-120K

Common Values

ValueCountFrequency (%)
below 40K 3327
32.9%
40-60K 1666
16.5%
80-120K 1436
14.2%
60-80K 1302
 
12.9%
Unknown 1030
 
10.2%
120K and more 685
 
6.8%
(Missing) 681
 
6.7%

Length

2023-05-01T14:57:14.525520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T14:57:14.639440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
below 3327
23.5%
40k 3327
23.5%
40-60k 1666
11.8%
80-120k 1436
10.2%
60-80k 1302
 
9.2%
unknown 1030
 
7.3%
120k 685
 
4.8%
and 685
 
4.8%
more 685
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 12820
17.3%
K 8416
11.4%
o 5042
 
6.8%
4 4993
 
6.8%
4697
 
6.4%
- 4404
 
6.0%
w 4357
 
5.9%
e 4012
 
5.4%
n 3775
 
5.1%
b 3327
 
4.5%
Other values (11) 18075
24.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27761
37.6%
Lowercase Letter 27610
37.4%
Uppercase Letter 9446
 
12.8%
Space Separator 4697
 
6.4%
Dash Punctuation 4404
 
6.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 5042
18.3%
w 4357
15.8%
e 4012
14.5%
n 3775
13.7%
b 3327
12.0%
l 3327
12.0%
k 1030
 
3.7%
a 685
 
2.5%
d 685
 
2.5%
m 685
 
2.5%
Decimal Number
ValueCountFrequency (%)
0 12820
46.2%
4 4993
 
18.0%
6 2968
 
10.7%
8 2738
 
9.9%
1 2121
 
7.6%
2 2121
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
K 8416
89.1%
U 1030
 
10.9%
Space Separator
ValueCountFrequency (%)
4697
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4404
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37056
50.1%
Common 36862
49.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 8416
22.7%
o 5042
13.6%
w 4357
11.8%
e 4012
10.8%
n 3775
10.2%
b 3327
 
9.0%
l 3327
 
9.0%
U 1030
 
2.8%
k 1030
 
2.8%
a 685
 
1.8%
Other values (3) 2055
 
5.5%
Common
ValueCountFrequency (%)
0 12820
34.8%
4 4993
 
13.5%
4697
 
12.7%
- 4404
 
11.9%
6 2968
 
8.1%
8 2738
 
7.4%
1 2121
 
5.8%
2 2121
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12820
17.3%
K 8416
11.4%
o 5042
 
6.8%
4 4993
 
6.8%
4697
 
6.4%
- 4404
 
6.0%
w 4357
 
5.9%
e 4012
 
5.4%
n 3775
 
5.1%
b 3327
 
4.5%
Other values (11) 18075
24.5%
Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.928409
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-05-01T14:57:14.751738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile22
Q131
median36
Q340
95-th percentile50
Maximum56
Range43
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.9864163
Coefficient of variation (CV)0.22228695
Kurtosis0.40010012
Mean35.928409
Median Absolute Deviation (MAD)4
Skewness-0.10656536
Sum363847
Variance63.782846
MonotonicityNot monotonic
2023-05-01T14:57:14.863722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
36 2463
24.3%
37 358
 
3.5%
34 353
 
3.5%
38 347
 
3.4%
39 341
 
3.4%
40 333
 
3.3%
31 318
 
3.1%
35 317
 
3.1%
33 305
 
3.0%
30 300
 
3.0%
Other values (34) 4692
46.3%
ValueCountFrequency (%)
13 70
0.7%
14 16
 
0.2%
15 34
 
0.3%
16 29
 
0.3%
17 39
 
0.4%
18 58
0.6%
19 63
0.6%
20 74
0.7%
21 83
0.8%
22 105
1.0%
ValueCountFrequency (%)
56 103
1.0%
55 42
 
0.4%
54 53
 
0.5%
53 78
0.8%
52 62
 
0.6%
51 80
0.8%
50 96
0.9%
49 141
1.4%
48 162
1.6%
47 171
1.7%
Distinct6298
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10036.344
Minimum1438.3
Maximum310644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-05-01T14:57:14.977446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1438.3
5-th percentile1449
Q12578.5
median4696
Q311767.5
95-th percentile34516
Maximum310644
Range309205.7
Interquartile range (IQR)9189

Descriptive statistics

Standard deviation17629.707
Coefficient of variation (CV)1.7565866
Kurtosis139.53768
Mean10036.344
Median Absolute Deviation (MAD)2732
Skewness9.7414553
Sum1.0163805 × 108
Variance3.1080658 × 108
MonotonicityNot monotonic
2023-05-01T14:57:15.230792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34516 497
 
4.9%
1438.3 496
 
4.9%
15987 18
 
0.2%
9959 17
 
0.2%
23981 12
 
0.1%
12944.7 11
 
0.1%
6224 11
 
0.1%
310644 11
 
0.1%
3735 10
 
0.1%
2490 10
 
0.1%
Other values (6288) 9034
89.2%
ValueCountFrequency (%)
1438.3 496
4.9%
1439 2
 
< 0.1%
1440 1
 
< 0.1%
1441 2
 
< 0.1%
1442 1
 
< 0.1%
1443 3
 
< 0.1%
1446 1
 
< 0.1%
1449 2
 
< 0.1%
1451 2
 
< 0.1%
1452 2
 
< 0.1%
ValueCountFrequency (%)
310644 11
0.1%
298098 1
 
< 0.1%
296334 1
 
< 0.1%
269667 1
 
< 0.1%
265959 1
 
< 0.1%
258057 1
 
< 0.1%
257445 1
 
< 0.1%
232938 1
 
< 0.1%
230409 1
 
< 0.1%
224883 1
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Blue
9436 
Silver
 
555
Gold
 
116
Platinum
 
20

Length

Max length8
Median length4
Mean length4.1175077
Min length4

Characters and Unicode

Total characters41698
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowBlue
3rd rowSilver
4th rowBlue
5th rowBlue

Common Values

ValueCountFrequency (%)
Blue 9436
93.2%
Silver 555
 
5.5%
Gold 116
 
1.1%
Platinum 20
 
0.2%

Length

2023-05-01T14:57:15.353845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T14:57:15.458789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
blue 9436
93.2%
silver 555
 
5.5%
gold 116
 
1.1%
platinum 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31571
75.7%
Uppercase Letter 10127
 
24.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 10127
32.1%
e 9991
31.6%
u 9456
30.0%
i 575
 
1.8%
v 555
 
1.8%
r 555
 
1.8%
o 116
 
0.4%
d 116
 
0.4%
a 20
 
0.1%
t 20
 
0.1%
Other values (2) 40
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
B 9436
93.2%
S 555
 
5.5%
G 116
 
1.1%
P 20
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 41698
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41698
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
B 9436
22.6%
i 575
 
1.4%
S 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
G 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

total_products
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1473289
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-05-01T14:57:15.540880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum36
Range35
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.1834766
Coefficient of variation (CV)0.76759686
Kurtosis50.052262
Mean4.1473289
Median Absolute Deviation (MAD)1
Skewness6.0283963
Sum42000
Variance10.134524
MonotonicityNot monotonic
2023-05-01T14:57:15.625054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 2264
22.4%
4 1874
18.5%
5 1863
18.4%
6 1847
18.2%
2 1221
12.1%
1 895
 
8.8%
18 41
 
0.4%
24 38
 
0.4%
36 34
 
0.3%
30 28
 
0.3%
ValueCountFrequency (%)
1 895
 
8.8%
2 1221
12.1%
3 2264
22.4%
4 1874
18.5%
5 1863
18.4%
6 1847
18.2%
12 22
 
0.2%
18 41
 
0.4%
24 38
 
0.4%
30 28
 
0.3%
ValueCountFrequency (%)
36 34
 
0.3%
30 28
 
0.3%
24 38
 
0.4%
18 41
 
0.4%
12 22
 
0.2%
6 1847
18.2%
5 1863
18.4%
4 1874
18.5%
3 2264
22.4%
2 1221
12.1%

period_inactive
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3411672
Minimum0
Maximum6
Zeros29
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-05-01T14:57:15.714314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0106224
Coefficient of variation (CV)0.4316746
Kurtosis1.0985226
Mean2.3411672
Median Absolute Deviation (MAD)1
Skewness0.63306113
Sum23709
Variance1.0213576
MonotonicityNot monotonic
2023-05-01T14:57:15.789917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3846
38.0%
2 3282
32.4%
1 2233
22.0%
4 435
 
4.3%
5 178
 
1.8%
6 124
 
1.2%
0 29
 
0.3%
ValueCountFrequency (%)
0 29
 
0.3%
1 2233
22.0%
2 3282
32.4%
3 3846
38.0%
4 435
 
4.3%
5 178
 
1.8%
6 124
 
1.2%
ValueCountFrequency (%)
6 124
 
1.2%
5 178
 
1.8%
4 435
 
4.3%
3 3846
38.0%
2 3282
32.4%
1 2233
22.0%
0 29
 
0.3%

contacts_in_last_year
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4553175
Minimum0
Maximum6
Zeros399
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-05-01T14:57:15.874998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1062251
Coefficient of variation (CV)0.45054261
Kurtosis0.00086265663
Mean2.4553175
Median Absolute Deviation (MAD)1
Skewness0.011005626
Sum24865
Variance1.2237341
MonotonicityNot monotonic
2023-05-01T14:57:15.959986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3380
33.4%
2 3227
31.9%
1 1499
14.8%
4 1392
13.7%
0 399
 
3.9%
5 176
 
1.7%
6 54
 
0.5%
ValueCountFrequency (%)
0 399
 
3.9%
1 1499
14.8%
2 3227
31.9%
3 3380
33.4%
4 1392
13.7%
5 176
 
1.7%
6 54
 
0.5%
ValueCountFrequency (%)
6 54
 
0.5%
5 176
 
1.7%
4 1392
13.7%
3 3380
33.4%
2 3227
31.9%
1 1499
14.8%
0 399
 
3.9%

credit_card_debt_balance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1974
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.8141
Minimum0
Maximum2517
Zeros2470
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-05-01T14:57:16.076772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1359
median1276
Q31784
95-th percentile2517
Maximum2517
Range2517
Interquartile range (IQR)1425

Descriptive statistics

Standard deviation814.98734
Coefficient of variation (CV)0.70087503
Kurtosis-1.1459918
Mean1162.8141
Median Absolute Deviation (MAD)591
Skewness-0.14883725
Sum11775818
Variance664204.36
MonotonicityNot monotonic
2023-05-01T14:57:16.203857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2470
 
24.4%
2517 508
 
5.0%
1480 12
 
0.1%
1965 12
 
0.1%
1434 11
 
0.1%
1720 11
 
0.1%
1664 11
 
0.1%
1482 10
 
0.1%
1590 10
 
0.1%
1250 10
 
0.1%
Other values (1964) 7062
69.7%
ValueCountFrequency (%)
0 2470
24.4%
132 1
 
< 0.1%
134 1
 
< 0.1%
145 1
 
< 0.1%
154 1
 
< 0.1%
157 1
 
< 0.1%
159 2
 
< 0.1%
168 2
 
< 0.1%
170 1
 
< 0.1%
186 1
 
< 0.1%
ValueCountFrequency (%)
2517 508
5.0%
2514 3
 
< 0.1%
2513 1
 
< 0.1%
2512 2
 
< 0.1%
2511 1
 
< 0.1%
2509 2
 
< 0.1%
2508 2
 
< 0.1%
2507 4
 
< 0.1%
2506 1
 
< 0.1%
2505 3
 
< 0.1%

remaining_credit_limit
Real number (ℝ)

Distinct6813
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7469.1396
Minimum3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-05-01T14:57:16.333232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile480.3
Q11324.5
median3474
Q39859
95-th percentile32183.4
Maximum34516
Range34513
Interquartile range (IQR)8534.5

Descriptive statistics

Standard deviation9090.6853
Coefficient of variation (CV)1.2170994
Kurtosis1.7986173
Mean7469.1396
Median Absolute Deviation (MAD)2665
Skewness1.6616965
Sum75639977
Variance82640560
MonotonicityNot monotonic
2023-05-01T14:57:16.456436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3 324
 
3.2%
34516 98
 
1.0%
31999 26
 
0.3%
787 8
 
0.1%
463 7
 
0.1%
713 7
 
0.1%
953 7
 
0.1%
701 7
 
0.1%
740 6
 
0.1%
1623 6
 
0.1%
Other values (6803) 9631
95.1%
ValueCountFrequency (%)
3 1
< 0.1%
10 1
< 0.1%
14 2
< 0.1%
15 1
< 0.1%
24 1
< 0.1%
28 1
< 0.1%
29 1
< 0.1%
36 1
< 0.1%
39 2
< 0.1%
41 2
< 0.1%
ValueCountFrequency (%)
34516 98
1.0%
34362 1
 
< 0.1%
34302 1
 
< 0.1%
34300 1
 
< 0.1%
34297 1
 
< 0.1%
34286 1
 
< 0.1%
34238 1
 
< 0.1%
34227 1
 
< 0.1%
34140 1
 
< 0.1%
34119 1
 
< 0.1%

transaction_amount_ratio
Real number (ℝ)

Distinct1158
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75994065
Minimum0
Maximum3.397
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-05-01T14:57:16.583940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.463
Q10.631
median0.736
Q30.859
95-th percentile1.103
Maximum3.397
Range3.397
Interquartile range (IQR)0.228

Descriptive statistics

Standard deviation0.21920677
Coefficient of variation (CV)0.28845248
Kurtosis9.9935012
Mean0.75994065
Median Absolute Deviation (MAD)0.114
Skewness1.7320634
Sum7695.919
Variance0.048051608
MonotonicityNot monotonic
2023-05-01T14:57:16.708811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.791 36
 
0.4%
0.712 34
 
0.3%
0.743 34
 
0.3%
0.718 33
 
0.3%
0.735 33
 
0.3%
0.744 32
 
0.3%
0.722 32
 
0.3%
0.699 32
 
0.3%
0.69 31
 
0.3%
0.631 31
 
0.3%
Other values (1148) 9799
96.8%
ValueCountFrequency (%)
0 5
< 0.1%
0.01 1
 
< 0.1%
0.018 1
 
< 0.1%
0.046 1
 
< 0.1%
0.061 2
 
< 0.1%
0.072 1
 
< 0.1%
0.101 1
 
< 0.1%
0.12 1
 
< 0.1%
0.153 1
 
< 0.1%
0.163 1
 
< 0.1%
ValueCountFrequency (%)
3.397 1
< 0.1%
3.355 1
< 0.1%
2.675 1
< 0.1%
2.594 1
< 0.1%
2.368 1
< 0.1%
2.357 1
< 0.1%
2.316 1
< 0.1%
2.282 1
< 0.1%
2.275 1
< 0.1%
2.271 1
< 0.1%

total_transaction_amount
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5141
Distinct (%)52.9%
Missing407
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean5253.7119
Minimum510
Maximum117159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-05-01T14:57:16.838608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum510
5-th percentile1289.95
Q12196
median3971
Q34823
95-th percentile14994.25
Maximum117159
Range116649
Interquartile range (IQR)2627

Descriptive statistics

Standard deviation7402.2599
Coefficient of variation (CV)1.4089581
Kurtosis95.20208
Mean5253.7119
Median Absolute Deviation (MAD)1404
Skewness8.2080929
Sum51066080
Variance54793452
MonotonicityNot monotonic
2023-05-01T14:57:16.958486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4253 11
 
0.1%
4509 10
 
0.1%
4518 9
 
0.1%
4037 9
 
0.1%
2229 9
 
0.1%
4498 9
 
0.1%
4313 9
 
0.1%
4869 8
 
0.1%
4674 8
 
0.1%
4220 8
 
0.1%
Other values (5131) 9630
95.1%
(Missing) 407
 
4.0%
ValueCountFrequency (%)
510 1
< 0.1%
530 1
< 0.1%
563 1
< 0.1%
569 1
< 0.1%
594 1
< 0.1%
596 1
< 0.1%
597 1
< 0.1%
602 1
< 0.1%
615 1
< 0.1%
643 1
< 0.1%
ValueCountFrequency (%)
117159 1
< 0.1%
114233 1
< 0.1%
113806 1
< 0.1%
108864 1
< 0.1%
108591 1
< 0.1%
108101 1
< 0.1%
107793 1
< 0.1%
106701 1
< 0.1%
106582 1
< 0.1%
105273 1
< 0.1%

total_transaction_count
Real number (ℝ)

Distinct126
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.858695
Minimum10
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-05-01T14:57:17.076489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile28
Q145
median67
Q381
95-th percentile105
Maximum139
Range129
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.47257
Coefficient of variation (CV)0.36190322
Kurtosis-0.36716324
Mean64.858695
Median Absolute Deviation (MAD)17
Skewness0.15367307
Sum656824
Variance550.96156
MonotonicityNot monotonic
2023-05-01T14:57:17.204141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 208
 
2.1%
75 203
 
2.0%
71 203
 
2.0%
69 202
 
2.0%
82 202
 
2.0%
76 198
 
2.0%
77 197
 
1.9%
70 193
 
1.9%
78 190
 
1.9%
74 190
 
1.9%
Other values (116) 8141
80.4%
ValueCountFrequency (%)
10 4
 
< 0.1%
11 2
 
< 0.1%
12 4
 
< 0.1%
13 5
 
< 0.1%
14 9
 
0.1%
15 16
0.2%
16 13
0.1%
17 13
0.1%
18 23
0.2%
19 11
0.1%
ValueCountFrequency (%)
139 1
 
< 0.1%
138 1
 
< 0.1%
134 1
 
< 0.1%
132 1
 
< 0.1%
131 6
0.1%
130 5
< 0.1%
129 6
0.1%
128 10
0.1%
127 12
0.1%
126 10
0.1%

transaction_count_ratio
Real number (ℝ)

Distinct1071
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81754439
Minimum0
Maximum16.25
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-05-01T14:57:17.333447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3713
Q10.588
median0.711
Q30.838
95-th percentile1.3817
Maximum16.25
Range16.25
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.61990619
Coefficient of variation (CV)0.75825387
Kurtosis60.398502
Mean0.81754439
Median Absolute Deviation (MAD)0.124
Skewness5.7033308
Sum8279.272
Variance0.38428369
MonotonicityNot monotonic
2023-05-01T14:57:17.460638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 163
 
1.6%
0.667 159
 
1.6%
0.5 156
 
1.5%
0.75 147
 
1.5%
0.6 109
 
1.1%
0.8 95
 
0.9%
0.714 91
 
0.9%
0.833 82
 
0.8%
0.778 67
 
0.7%
0.625 61
 
0.6%
Other values (1061) 8997
88.8%
ValueCountFrequency (%)
0 7
0.1%
0.028 1
 
< 0.1%
0.029 1
 
< 0.1%
0.038 1
 
< 0.1%
0.053 1
 
< 0.1%
0.059 2
 
< 0.1%
0.062 1
 
< 0.1%
0.074 1
 
< 0.1%
0.077 3
< 0.1%
0.091 3
< 0.1%
ValueCountFrequency (%)
16.25 1
< 0.1%
8.75 1
< 0.1%
7.855 1
< 0.1%
6.175 1
< 0.1%
5.945 1
< 0.1%
5.87 1
< 0.1%
5.835 1
< 0.1%
5.64 1
< 0.1%
5.455 1
< 0.1%
5.385 1
< 0.1%

average_utilization
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct964
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27489355
Minimum0
Maximum0.999
Zeros2470
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2023-05-01T14:57:17.749111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.023
median0.176
Q30.503
95-th percentile0.793
Maximum0.999
Range0.999
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.27569147
Coefficient of variation (CV)1.0029026
Kurtosis-0.79497195
Mean0.27489355
Median Absolute Deviation (MAD)0.176
Skewness0.718008
Sum2783.847
Variance0.076005786
MonotonicityNot monotonic
2023-05-01T14:57:17.871563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2470
 
24.4%
0.073 44
 
0.4%
0.057 33
 
0.3%
0.048 32
 
0.3%
0.06 30
 
0.3%
0.045 29
 
0.3%
0.061 29
 
0.3%
0.069 28
 
0.3%
0.059 28
 
0.3%
0.053 27
 
0.3%
Other values (954) 7377
72.8%
ValueCountFrequency (%)
0 2470
24.4%
0.004 1
 
< 0.1%
0.005 1
 
< 0.1%
0.006 3
 
< 0.1%
0.007 1
 
< 0.1%
0.008 2
 
< 0.1%
0.009 1
 
< 0.1%
0.01 1
 
< 0.1%
0.011 1
 
< 0.1%
0.012 4
 
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.992 1
 
< 0.1%
0.99 1
 
< 0.1%
0.988 1
 
< 0.1%
0.987 1
 
< 0.1%
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 4
< 0.1%

account_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
open
8500 
closed
1627 

Length

Max length6
Median length4
Mean length4.3213192
Min length4

Characters and Unicode

Total characters43762
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowopen
2nd rowclosed
3rd rowopen
4th rowopen
5th rowopen

Common Values

ValueCountFrequency (%)
open 8500
83.9%
closed 1627
 
16.1%

Length

2023-05-01T14:57:17.989825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-01T14:57:18.086851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
open 8500
83.9%
closed 1627
 
16.1%

Most occurring characters

ValueCountFrequency (%)
o 10127
23.1%
e 10127
23.1%
p 8500
19.4%
n 8500
19.4%
c 1627
 
3.7%
l 1627
 
3.7%
s 1627
 
3.7%
d 1627
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43762
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 10127
23.1%
e 10127
23.1%
p 8500
19.4%
n 8500
19.4%
c 1627
 
3.7%
l 1627
 
3.7%
s 1627
 
3.7%
d 1627
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 43762
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 10127
23.1%
e 10127
23.1%
p 8500
19.4%
n 8500
19.4%
c 1627
 
3.7%
l 1627
 
3.7%
s 1627
 
3.7%
d 1627
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 10127
23.1%
e 10127
23.1%
p 8500
19.4%
n 8500
19.4%
c 1627
 
3.7%
l 1627
 
3.7%
s 1627
 
3.7%
d 1627
 
3.7%

Interactions

2023-05-01T14:57:10.480228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:44.107561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:46.068179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:47.835892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:49.821943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:51.543328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:53.424999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:55.450578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:57.219324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:59.179205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:00.928837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:02.784797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:04.826607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:06.644358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:08.626768image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:10.590327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:44.264412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:46.181997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:47.948140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:49.946932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:51.663177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:53.555655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:55.564907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:57.331723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:59.288547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:01.048340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:03.073010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:04.943108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:06.757732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:08.745220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:10.697212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:44.375880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:46.290733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:48.058010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:50.062982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:51.780128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:53.670943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:55.674247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:57.440622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:59.400218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:01.165918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:03.185728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:05.053981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:06.873186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:08.862049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:10.810491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:44.492377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:46.402862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:48.175678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:50.176077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:51.900352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:53.787432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:55.787770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:57.554482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:59.513283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:01.287887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:03.305501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:05.171867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:06.990758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:08.983678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:10.917341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:44.602659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:46.509660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:48.288539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:50.278448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:52.016727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:53.904584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:55.894480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:57.661323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:59.620149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:01.408008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:03.421267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:05.288023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:07.107152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:09.098936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:11.034964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:44.722294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:46.635132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:48.412234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:50.393429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:52.151763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:54.211755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:56.015326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:57.781867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:59.738482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:01.536301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:03.556388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:05.416854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:07.229012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:09.223148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:11.152909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:45.004729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:46.752250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:48.541353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:50.509749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:52.290494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:54.341718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:56.136493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:57.905006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:59.858879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:01.663860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:03.693428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:05.543320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:07.520959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:09.349720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:11.273746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:45.123313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:46.867471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:48.665459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:50.623251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:52.412812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:54.464046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:56.257718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:58.022114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:59.974337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:01.785596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:03.816914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:05.664722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:07.643038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:09.470975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:11.392876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:45.239141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:46.982881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:48.784083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:50.734468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:52.536580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:54.591601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:56.378486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:58.140593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:00.091661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:01.909470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:03.943366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:05.783184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:07.767500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:09.594420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:11.508394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:45.349282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:47.093331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:48.899171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:50.847115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:52.653476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:54.708577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:56.494488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:58.253705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:00.207102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:02.026475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:04.061757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:05.901772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:07.884978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:09.719275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:11.632488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:45.468502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:47.215681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:49.023638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:50.962224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:52.782254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:54.834455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:56.618227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:58.563439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:00.330387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:02.154569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:04.188534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:06.029680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:08.015580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:09.849115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:11.929533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:45.588474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:47.343238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:49.145723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:51.081748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:52.910212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:54.957714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:56.740191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:58.688931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:00.452006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:02.283668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:04.319942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:06.154505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:08.145830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:09.982146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:12.048647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:45.706336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:47.464136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:49.264996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:51.194295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:53.034317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:55.080761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:56.858433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:58.809942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:00.571755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:02.410458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:04.447591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:06.277838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:08.265304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:10.106950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:12.164534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:45.821768image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:47.582105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:49.569972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:51.307952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:53.164269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:55.204129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:56.977339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:58.933857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:00.688884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:02.533690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:04.571769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:06.397345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:08.385746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:10.228851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:12.287908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:45.954694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:47.724418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:49.700799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:51.435380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:53.302061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:55.332583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:57.105353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:56:59.061957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:00.810542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:02.662759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:04.705778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:06.526340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:08.511019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-05-01T14:57:10.359656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2023-05-01T14:57:18.189887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
customer_idcustomer_agecustomer_number_of_dependentscustomer_relationship_lengthcustomer_available_credit_limittotal_productsperiod_inactivecontacts_in_last_yearcredit_card_debt_balanceremaining_credit_limittransaction_amount_ratiototal_transaction_amounttotal_transaction_counttransaction_count_ratioaverage_utilizationcustomer_sexcustomer_educationcustomer_civil_statuscustomer_salary_rangecredit_card_classificationaccount_status
customer_id1.000-0.0050.010-0.0010.012-0.0060.025-0.0030.0020.007-0.005-0.0050.003-0.002-0.0000.0000.0000.0000.0000.0000.022
customer_age-0.0051.000-0.1410.7690.004-0.0170.042-0.0150.012-0.002-0.070-0.033-0.052-0.0320.0100.0000.0160.0800.0830.0230.022
customer_number_of_dependents0.010-0.1411.000-0.1150.045-0.037-0.009-0.041-0.0040.054-0.0260.0560.0530.006-0.0350.0080.0010.0370.0460.0180.021
customer_relationship_length-0.0010.769-0.1151.0000.009-0.0150.057-0.0080.0060.008-0.054-0.029-0.039-0.032-0.0040.0000.0020.0430.0460.0130.019
customer_available_credit_limit0.0120.0040.0450.0091.000-0.054-0.0250.0240.1250.9050.0210.0250.032-0.005-0.4080.2190.0000.0200.1650.2900.000
total_products-0.006-0.017-0.037-0.015-0.0541.000-0.0100.0630.017-0.0650.022-0.258-0.2190.0260.0650.0120.0070.0000.0000.0250.097
period_inactive0.0250.042-0.0090.057-0.025-0.0101.0000.030-0.043-0.016-0.019-0.027-0.051-0.046-0.0270.0120.0000.0070.0200.0000.196
contacts_in_last_year-0.003-0.015-0.041-0.0080.0240.0630.0301.000-0.0450.033-0.021-0.162-0.168-0.083-0.0590.0640.0000.0070.0150.0100.239
credit_card_debt_balance0.0020.012-0.0040.0060.1250.017-0.043-0.0451.000-0.1540.0360.0180.0400.0710.7090.0300.0070.0120.0240.0190.402
remaining_credit_limit0.007-0.0020.0540.0080.905-0.065-0.0160.033-0.1541.0000.0070.0220.022-0.032-0.6860.4420.0000.0280.2770.3370.019
transaction_amount_ratio-0.005-0.070-0.026-0.0540.0210.022-0.019-0.0210.0360.0071.0000.1280.0850.2760.0330.0440.0000.0530.0130.0240.184
total_transaction_amount-0.005-0.0330.056-0.0290.025-0.258-0.027-0.1620.0180.0220.1281.0000.8310.1890.0170.0830.0000.0240.0330.1010.111
total_transaction_count0.003-0.0520.053-0.0390.032-0.219-0.051-0.1680.0400.0220.0850.8311.0000.2150.0400.1670.0040.0990.0540.1090.458
transaction_count_ratio-0.002-0.0320.006-0.032-0.0050.026-0.046-0.0830.071-0.0320.2760.1890.2151.0000.0820.0120.0000.0000.0080.0000.019
average_utilization-0.0000.010-0.035-0.004-0.4080.065-0.027-0.0590.709-0.6860.0330.0170.0400.0821.0000.2830.0000.0270.1630.1490.241
customer_sex0.0000.0000.0080.0000.2190.0120.0120.0640.0300.4420.0440.0830.1670.0120.2831.0000.0120.0090.8360.0860.032
customer_education0.0000.0160.0010.0020.0000.0070.0000.0000.0070.0000.0000.0000.0040.0000.0000.0121.0000.0110.0210.0000.025
customer_civil_status0.0000.0800.0370.0430.0200.0000.0070.0070.0120.0280.0530.0240.0990.0000.0270.0090.0111.0000.0000.0280.017
customer_salary_range0.0000.0830.0460.0460.1650.0000.0200.0150.0240.2770.0130.0330.0540.0080.1630.8360.0210.0001.0000.0520.029
credit_card_classification0.0000.0230.0180.0130.2900.0250.0000.0100.0190.3370.0240.1010.1090.0000.1490.0860.0000.0280.0521.0000.000
account_status0.0220.0220.0210.0190.0000.0970.1960.2390.4020.0190.1840.1110.4580.0190.2410.0320.0250.0170.0290.0001.000

Missing values

2023-05-01T14:57:12.496818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-01T14:57:12.821242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-01T14:57:13.074000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

customer_idcustomer_agecustomer_sexcustomer_number_of_dependentscustomer_educationcustomer_civil_statuscustomer_salary_rangecustomer_relationship_lengthcustomer_available_credit_limitcredit_card_classificationtotal_productsperiod_inactivecontacts_in_last_yearcredit_card_debt_balanceremaining_credit_limittransaction_amount_ratiototal_transaction_amounttotal_transaction_counttransaction_count_ratioaverage_utilizationaccount_status
075541038.0F2High SchoolMarried40-60K311593.0Blue4241091502.00.8704136.0670.7180.685open
156809346.0F2GraduateUnknownbelow 40K406568.0Blue52206568.00.1011507.0330.2220.000closed
259538943.0M1High SchoolMarried80-120K3034516.0Silver113204532471.00.5904081.0540.4210.059open
328725246.0F4High SchoolMarriedbelow 40K362374.0Blue52113321042.00.6864253.0810.8840.561open
423190140.0M4High SchoolSingle80-120K2912978.0Blue332012978.00.62814134.0850.7000.000open
541882151.0M4High SchoolMarried80-120K4214438.0Blue612251711921.00.8532090.0470.8800.174open
689618745.0F3UneducatedSinglebelow 40K392551.0Blue5412253298.00.688NaN630.9090.883open
725849550.0F1High SchoolSinglebelow 40K364517.0Blue11322382279.00.6254686.0810.7610.495open
888172045.0F5UnknownMarriedUnknown4014728.0Blue322014728.00.7084660.0850.7350.000open
936725140.0F2GraduateUnknownbelow 40K292636.0Blue5121953683.00.9164584.0700.5910.741open
customer_idcustomer_agecustomer_sexcustomer_number_of_dependentscustomer_educationcustomer_civil_statuscustomer_salary_rangecustomer_relationship_lengthcustomer_available_credit_limitcredit_card_classificationtotal_productsperiod_inactivecontacts_in_last_yearcredit_card_debt_balanceremaining_credit_limittransaction_amount_ratiototal_transaction_amounttotal_transaction_counttransaction_count_ratioaverage_utilizationaccount_status
1011737894437.0F3UnknownMarriedbelow 40K251438.3Blue621674764.32.1801717.0310.7220.469open
1011894820141.0NaN4High SchoolSingle60-80K284475.0Blue61404475.00.7063367.0660.7370.000open
1011947681648.0F2UneducatedUnknownbelow 40K422055.0Blue32202055.01.06425592.0630.7500.000open
10120527751NaNM4Post-GraduateMarried40-60K362219.0Blue12211801039.00.7688686.0910.6250.532open
1012192146348.0F5Post-GraduateMarriedbelow 40K401438.3Blue333665773.30.7504100.0840.6150.462open
10122435491NaNF2High SchoolSingle40-60K361677.0Blue23101677.00.7004035.0870.8910.000open
1012313605246.0F4UneducatedSingleUnknown33224541.0Blue333174923200.00.6264092.0770.5400.070open
1012433658344.0M0High SchoolSingle60-80K366606.0Blue63225174089.00.8254493.0680.6590.381open
1012581712939.0F5GraduateSinglebelow 40K282077.0Blue33202077.00.5903647.0690.9170.000open
1012640412035.0M2Post-GraduateMarried40-60K212256.0Blue3137751481.00.8791749.0330.4350.344open